Agentic vs Non-Agentic

If I ask you to write an essay on a certain topic but given the condition that you write it in one go, express whatever comes to your mind on that topic at first, you are not allowed to press backspace, and also expecting best quality result.

Most of the LLMs we use at present work like this, using zero shot mode, prompting the model to generate final output without revising its work. Although this is a difficult task LLMs do this task well. This is non-agentic workflow.

In continuation to the example, if I allow you to think over, iterate over ideas and re-write the essay, you are going to do much better and as a result produce high quality output. Similarly with agent workflow we can ask the LLM to iterate over the topic many times.

How this can help achieve more efficiency ?

Imagine you have a research paper to write. You use a search engine like Google Scholar, which is a non-agentic AI tool. You give it a specific question (like "AI and health care"), and it provides results (research papers on the topic). You then have to sort through these results, read and analyze them yourself to write your paper. This will be your whole human effort, now how AI agents can help you excel at this -

The Future of AI: Agentic Workflows

The AI acts more like an assistant or collaborator. Continuing the research paper example, an agentic AI tool might:

• Search for relevant papers like the non-agentic search engine, do a web search and gather more information.

• Analyze the papers and summarize the key findings.

• Identify important arguments and opposing viewpoints.

• Even help you write a draft of your paper.

• Read over the first draft to spot unjustified arguments or extraneous information.

• Revise the draft taking into account any shortcoming spotted.

• and continue further...

Basically, the AI takes initiative and does more than just respond to your specific commands.